这篇文章介绍了C2S-Scale，这是一个基于大型语言模型（LLMs）的家族，专为下一代单细胞分析而设计。C2S-Scale通过将单细胞RNA测序（scRNA-seq）数据转化为文本格式的“细胞句子”，从而能够利用LLMs处理超过十亿个转录组数据、生物学文本和元数据的语料库。通过将模型扩展到270亿个参数，C2S-Scale在预测和生成能力上表现出持续的改进，并在细胞类型注释、多细胞上下文推理、自然语言解释以及扰动反应预测等多样化任务中超越了现有的专业单细胞模型和通用LLMs。此外，通过一个双上下文虚拟筛选，该模型预测并实验验证了激酶抑制剂silmitasertib作为抗原呈递的干扰素条件放大剂的新发现。References: Rizvi, Syed Asad, et al. "Scaling large language models for next-generation single-cell analysis." BioRxiv (2025): 2025-04.
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